---
title: "Climate Legislature Visualizations"
author: "Daniel Palamarchuk"
output:
flexdashboard::flex_dashboard:
orientation: columns
source_code: embed
theme: united
---
```{r setup, include=FALSE}
library(bslib)
library(flexdashboard)
library(plotly)
library(ggplot2)
library(plyr)
source("functions.R")
csv17 <- read.csv("data/csv_2017.csv", nrows = 89) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2017, Year_dis = paste(Year, Dis, sep = ""))
csv18 <- read.csv("data/csv_2018.csv", nrows = 111) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2018, Year_dis = paste(Year, Dis, sep = ""))
csv19 <- read.csv("data/csv_2019.csv", nrows = 92) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2019, Year_dis = paste(Year, Dis, sep = ""))
csv20 <- read.csv("data/csv_2020.csv", nrows = 92) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2020, Year_dis = paste(Year, Dis, sep = ""))
csv21 <- read.csv("data/csv_2021.csv", nrows = 92) %>% col_care() %>% add_identifiers() %>% mutate(Year = 2021, Year_dis = paste(Year, Dis, sep = ""))
csv_total <- rbind(csv17, csv18, csv19, csv20, csv21)
colors <- list("y2017" = "ef476f", "y2018" = "ffd166", "y2019" = "06d6a0", "y2020" = "118ab2", "y2021" = "073b4c",
"dead" = "6a5d5d")
labs <- c("Introduced", "Committee 1", "Floor 1",
"Committee 2", "Floor 2",
"Delivered to Governor", "Signed by Governor", "Law", "Dead")
```
General Overview
=====================================
These graphs provide a general overview of the overall data in VA between 2017-2021.
The graph on the left shows how the different bills move through the legislative process by year.
The graph on the right shows the same bills, except color-coded based on Sierra Club's position on the bill.
Column
-------------------------------------
### A kinda cool map
```{r}
joint_year <- rbind(
data_creator(csv17, colors$y2017),
data_creator(csv18, colors$y2018),
data_creator(csv19, colors$y2019),
data_creator(csv20, colors$y2020),
data_creator(csv21, colors$y2021)
)
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.2, 0.33, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.24, 0.17, 0.17, 0),
color = "gray",
pad = 10,
thickness = 10),
link = list(
source = as.numeric(joint_year$x) - 1,
target = as.numeric(joint_year$next_x) - 1,
value = joint_year$n,
color = ~as.factor(joint_year$color),
line = list(color = "black", width = 0.5)
))%>%
layout(title = "Annual Sankey with Joint Resolutions 2017-2021",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
Column
-------------------------------------
### Legend
```{r}
library(cowplot)
library(grid)
library(gridExtra)
library(ggpubr)
base_graph <- ggplot(csv_total, aes(Year_dis, fill = as.character(Year))) + geom_bar() +
scale_fill_manual(
values = c(paste("#",colors$y2017, sep = ""),paste("#",colors$y2018, sep = ""),paste("#",colors$y2019, sep = ""),
paste("#",colors$y2020, sep = ""),paste("#",colors$y2021, sep = ""))) +
theme_minimal() +
labs(title = "Fate of Different Bills in Relation to Sierra Club's Position in 2017 and 2018",
x = "Final Outcome of Bill", y = "Number of Bills",
fill = "Year") +
theme(legend.text = element_text(size = 15),
legend.title = element_text(size = 20),
legend.key.size = unit(2, "cm"))
legend_year <- get_legend(base_graph)
gg17 <- ggplot(csv17, aes(Dis, fill = Pos)) + geom_bar() +
scale_fill_manual(
values = c("Supported" = "#9ACD32", "Neutral" = "#B0E0E6", "Opposed" = "#FF4500")) +
theme_minimal() +
scale_x_discrete(limits = c("Died in Committee", "Died Elsewhere", "Passed into Law")) +
labs(title = "Fate of Different Bills in Relation to Sierra Club's Position in 2017 and 2018",
x = "Final Outcome of Bill", y = "Number of Bills",
fill = "Sierra Club Position") +
theme(legend.text = element_text(size = 15),
legend.title = element_text(size = 20),
legend.key.size = unit(2, "cm"))
legend_sierra <- get_legend(gg17)
ggarrange(legend_year, legend_sierra, ncol = 2)
```
### Sierra Club Sankey
```{r}
sierra_joint <- sierra_data(csv_total)
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.2, 0.33, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.24, 0.17, 0.17, 0),
color = "gray",
pad = 10,
thickness = 10), # 10 Pixel
link = list(
source = as.numeric(sierra_joint$x) - 1,
target = as.numeric(sierra_joint$next_x) - 1,
value = sierra_joint$n,
color = ~as.factor(sierra_joint$color),
line = list(color = "black", width = 0.5)
)) %>%
layout(title = "Sierra Sankey with Joint Resolutions 2017-2021",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
Annual Analysis
=====================================
```{r}
joint17 <- sierra_data(csv17)
joint18 <- sierra_data(csv18)
joint19 <- sierra_data(csv19)
joint20 <- sierra_data(csv20)
joint21 <- sierra_data(csv21)
```
Column {.tabset}
-------------------------------------
### 2017
```{r}
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.2, 0.37, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.20, 0.17, 0.2, 0),
color = "gray",
pad = 10,
thickness = 10),
link = list(
source = as.numeric(joint17$x) - 1,
target = as.numeric(joint17$next_x) - 1,
value = joint17$n,
color = ~as.factor(joint17$color),
line = list(color = "black", width = 0.5)
)) %>%
layout(title = "Annual Sankey with Joint Resolutions 2017",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
### 2018
```{r}
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.2, 0.37, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.10, 0.10, 0.10, 0.15, 0.07, 0.15, 0),
color = "gray",
pad = 10,
thickness = 10),
link = list(
source = as.numeric(joint18$x) - 1,
target = as.numeric(joint18$next_x) - 1,
value = joint18$n,
color = ~as.factor(joint18$color),
line = list(color = "black", width = 0.5)
)) %>%
layout(title = "Annual Sankey with Joint Resolutions 2018",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
### 2019
```{r}
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.2, 0.37, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.20, 0.17, 0.2, 0),
color = "gray",
pad = 10,
thickness = 10),
link = list(
source = as.numeric(joint19$x) - 1,
target = as.numeric(joint19$next_x) - 1,
value = joint19$n,
color = ~as.factor(joint19$color),
line = list(color = "black", width = 0.5)
)) %>%
layout(title = "Annual Sankey with Joint Resolutions 2019",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
### 2020
```{r}
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.25, 0.38, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.28, 0.18, 0.26, 0),
color = "gray",
pad = 10,
thickness = 10),
link = list(
source = as.numeric(joint20$x) - 1,
target = as.numeric(joint20$next_x) - 1,
value = joint20$n,
color = ~as.factor(joint20$color),
line = list(color = "black", width = 0.5)
)) %>%
layout(title = "Annual Sankey with Joint Resolutions 2020",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
### 2021
```{r}
plot_ly(
type = "sankey",
arrangement = "snap",
node = list(
label = labs,
x = c(0, 0.13, 0.25, 0.38, 0.5, 0.63, 0.81, 1, 1),
y = c(0.5, 0.49, 0.20, 0.20, 0.20, 0.37, 0.18, 0.31, 0),
color = "gray",
pad = 10,
thickness = 10),
link = list(
source = as.numeric(joint21$x) - 1,
target = as.numeric(joint21$next_x) - 1,
value = joint21$n,
color = ~as.factor(joint21$color),
line = list(color = "black", width = 0.5)
)) %>%
layout(title = "Individual Joint Graph for 2021",
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F),
showlegend = T)
```
Column
-------------------------------------
### General Overview
Over here we plot each year individually, showing the general structure in addition to how well received the bills were by climate activists.
An interesting and rather unsurprising trend is that 2020 and 2021 saw a huge increase in the amount of bills passed.
This can most likely be associated with Democratic control of both houses and the governorship, allowing democrats to push through more climate-favorable legislature more easily.
